Seven steps to prepare your network for AI

Seven steps to prepare your network for AI

AI is one of the hottest topics in the networking industry. On the one hand, it will have a transformative impact on optical network infrastructure, fundamentally reshaping how networks are managed, optimized and scaled. On the other hand, it will drive new capacity demand and the need to rearchitect the so-called middle mile of the network.

In his recent outlook, "2025 Trends to Watch" for the optical networking market, Ian Redpath, lead analyst at Omdia, states, "AI is catalyzing a generational optical-network build cycle. The largest cloud service providers are constructing gigawatt AI training centers, and communication service providers (CSPs) are responding with connectivity at Pbps scale." Ian predicts a significant bandwidth opportunity in interconnecting the enterprise data lakes with the AI training centers.

After a challenging year for the optical networking industry, it's essential to look for future growth opportunities. Optical transport solutions could be one such area. They will undoubtedly play a pivotal role in supporting emerging AI-driven middle-mile networks.

Here are seven reasons why and how these middle-mile networks will make a difference in addressing the unique demands of AI workloads:

1. High bandwidth and scalability

AI applications require immense data throughput to handle the vast amounts of data being generated and processed. AI-optimized networking solutions will rely on high-capacity optical transport systems that scale efficiently, ensuring middle-mile networks can meet the demands of AI operations without bottlenecks.

2. Low latency for real-time AI

AI applications, particularly those involving real-time decision-making, such as autonomous driving and smart city infrastructures, require extremely low latency. State-of-the-art optical transport systems minimize latency across middle-mile networks, enabling seamless data exchange for these latency-sensitive tasks.

3. Energy efficiency

Resource-intensive AI workloads can rapidly increase energy costs, so CSPs must focus on energy-efficient designs, which are critical for operators looking to maintain sustainable and cost-effective AI networks.

4. Flexibility and programmability

Flexible optical networking solutions are designed with software-defined networking (SDN) and automation capabilities. These features enable AI-driven middle-mile networks to dynamically adapt to changing data patterns, ensuring optimal performance and resource allocation.

Unlocking the coherent optical edge enables networks to scale with AI's explosive growth.5. Advanced modulation techniques

To optimize data transport over long distances, system vendors employ advanced modulation and signal processing techniques. This ensures reliable, high-quality signal transmission, even in challenging network environments, which is critical for AI workflows that depend on uninterrupted data streams.

6. Integration with edge and core AI requirements

An important aspect of optical networking design is the alignment with the increasing trend of edge-to-cloud AI processing. Transport systems bridge the edge (where data is collected) with the core (where data is processed and analyzed), ensuring that AI workloads are supported throughout the network.

7. Future-proofing for AI growth

AI and machine learning workloads are projected to grow exponentially, both in scale and complexity. CSPs need to make sure their transport solutions are built with future-proof capabilities, including support for next-generation optical technologies like low-power pluggable coherent optics, which provide even greater flexibility, capacity and reach.

At Adtran, we pride ourselves on "unlocking the coherent optical edge." We were the first vendor to bring coherent optical transmission technology to the network edge. Together with our technology partner Coherent, we developed the industry's first coherent 100ZR plug in a QSFP28 form factor with less than 5-watt power consumption suitable for outdoor cabinets.

What's more, we also developed a corresponding open line system (OLS) that eliminates unnecessary active components and is free of any spectral limitations. Our FSP 3000 Edge OLS has a minimal footprint and low power consumption, creating an energy-efficient and future-proof optical foundation.

Last but not least, our Mosaic One software suite unlocks SDN and automation capabilities.

Get ready for the demands of AI with Adtran

We're still in the early stages of deploying AI infrastructure, especially in the private AI cloud space. However, we're excited about the growth potential in this market segment, which is well aligned with our portfolio.

What sets us apart is our focus on optimizing networks specifically for AI applications. We provide tools and features such as network visibility, proactive fault management, and traffic engineering to ensure the network is robust and tailored for AI's dynamic needs. Our optical transport solution enables the high performance, efficiency and adaptability required for AI-driven middle-mile networks, ensuring they can keep pace with the explosive growth of AI technologies and applications.